A framework for engaging stakeholders in water quality modeling and management: Application to the Qu'Appelle River Basin, Canada

Journal of Environmental Management 231 (2019) 1117–1126

Contents lists available at ScienceDirect

Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman

Research article

A framework for engaging stakeholders in water quality modeling and management: Application to the Qu'Appelle River Basin, Canada

T

Elmira Hassanzadeha,b,∗, Graham Strickertb, Luis Morales-Marinb, Bram Nobleb, Helen Baulchb, Etienne Shupena-Soulodrec, Karl-Erich Lindenschmidtb a

Department of Civil, Geological and Mining Engineering, Polytechnique Montreal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada Global Institute for Water Security, University of Saskatchewan, 11 Innovation Blvd, Saskatoon, SK, S7N 3H5, Canada c Water Security Agency, 2365 Albert St #420, Regina, SK, S4P 4K1, Canada b

A R T I C LE I N FO

A B S T R A C T

Keywords: Water quality modeling and management Nutrient pollution Stakeholder engagement Agricultural Beneficial Management Practices (BMPs) Q-methodology System dynamics

Water quality is increasingly at risk due to nutrient pollution entering river systems from cities, industrial zones and agricultural areas. Agricultural activities are typically the largest non-point source of water pollution. The dynamics of agricultural impacts on water quality are complex and stem from the decisions and activities of multiple stakeholders, often with diverse business plans, values, and attitudes towards practices that can improve water quality. This study proposes a framework to understand and incorporate stakeholders' viewpoints into water quality modeling and management. The framework was applied to the Qu'Appelle River Basin, Saskatchewan, Canada. Q-methodology was used to understand viewpoints of stakeholders, namely agricultural producers (annual croppers, cattle producers, mixed farmers) and cottage owners, regarding a range of agricultural Beneficial Management Practices (BMPs) that can improve water quality, and to identify their preferred BMPs. A System Dynamics (SD) approach was employed to develop a transparent and user-friendly water quality model, SD-Qu'Appelle, to simulate nutrient loads in the region before and after implementation of stakeholder identified BMPs. The SD-Qu'Appelle was used in real-time engagement of stakeholders in model simulations to demonstrate and explore the potential effects of different BMPs in mitigating water pollution. Stakeholder perspectives were explored to understand the functionality and value of the SD-Qu'Appelle, preferred policies and potential barriers to BMP implementation on their land. Results show that although there are differences between viewpoints of stakeholders, they identified wetland restoration/retention, flow and erosion control, and relocation of corrals near creeks to sites more distant from waterways as the most effective BMPs for improving water quality. Economics was identified as a primary factor that causes agricultural producers to either accept or refuse the implementation of BMPs. Agricultural producers believe that incentives rather than regulations are the best policies for increasing the adoption of BMPs. Overall, stakeholders indicated the SD-Qu'Appelle had considerable value for water quality management and provided a set of recommendations to improve the model.

1. Introduction Agricultural activities are an important source of nutrients, contributing to issues of water pollution (Chapra, 2008). The most common approach to protect water quality in agricultural areas is to apply Beneficial Management Practices (BMPs) to reduce the amount of nutrients entering watercourses (Ji, 2017). Agricultural BMPs are mainly related to nutrient management; erosion and runoff control to prevent soil erosion and reduce the movement of nutrients; and installation of barriers and buffers to intercept sediments and nutrients transported from the field (Agriculture and Agri-food Canada, 2000).

Due to the critical role of agricultural producers in the implementation of BMPs, effective water quality management in agricultural areas requires understanding and incorporating their viewpoints about BMPs into decision-making processes. Agricultural producers' values can determine how different types of practices fit into the everyday business of running a farm, and whether or not a particular BMP will be adopted - regardless of how effective it might be at improving water quality. It is therefore crucial to understand agricultural producers' perspectives about water quality issues and management options so that any proposed BMP compliments, rather than contradicts, their underlying values and land management goals

∗ Corresponding author. Department of Civil, Geological and Mining Engineering, Polytechnique Montreal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada. E-mail address: [email protected] (E. Hassanzadeh).

https://doi.org/10.1016/j.jenvman.2018.11.016 Received 12 August 2018; Received in revised form 7 October 2018; Accepted 3 November 2018 0301-4797/ © 2018 Published by Elsevier Ltd.

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

(Davies and Hodge, 2007). An effective approach to identifying values and perspectives about BMPs is Q-methodology (Brown, 1996), where statements about a particular context are sorted by participants and analyzed by factor analysis to determine divergent and consensus issues (Webler et al., 2009). Q-methodology has been widely used to garner stakeholders' viewpoints to inform decision-making related to water security (e.g., Strickert et al., 2016); forest management (e.g., Steelman and Maguire, 1999); energy production (Cuppen et al., 2010); water quality management (e.g., Kerr and Bjornlund, 2011); reservoir operation (e.g., Qiu et al., 2014); and stormwater governance (e.g., Cousins, 2017). The method has also proven effective for exploring farmers' attitudes about agricultural BMPs (e.g., Bumbudsanpharoke et al., 2009; BaumgartGetz et al., 2012; Stang et al., 2016; Schall et al., 2018). Research has not identified a single factor explaining the adoption of practices (Prokopy et al., 2008); rather, an understanding of agricultural producers' viewpoints, concerns, and values is required to clearly comprehend alternative behavioural responses. Not only is it important that BMPs for improving water quality align with stakeholders' viewpoints, stakeholders should be involved in BMP assessment processes to ensure successful adoption (e.g., Inam et al., 2015; Palm-Forster et al., 2017). Engagement in assessment and selection processes provides those stakeholders who will ultimately implement BMPs with a sense of ownership and trust, and increases their commitment to BMP implementation on their land (e.g., Halbe et al., 2013; Balter and Adamowski, 2015). To meaningfully engage stakeholders in evaluating the efficacy of BMPs on water quality, a userfriendly model can be used to simulate water pollution before and after BMP implementation. A range of suitable approaches for integrated environmental modeling has been reviewed by Kelly et al. (2013). System Dynamics (SD; Forrester, 1961) is one of the more promising approaches to develop impact assessment models as it allows tracking the pattern of change in system behavior over time and space (Simonovic, 2009). SD environments allow representing multiple aspects of a system and the models can be improved over time as new information and system understanding emerge (Winz et al., 2009). SD has been widely used to simulate quantity and quality of water for scenario analysis and management purposes (e.g., Guo et al., 2001; Hassanzadeh et al., 2012; Hassanzadeh et al., 2015; Kotir et al., 2016; Gastelum et al., 2018). SD models facilitate representation of complex systems in a simple way, which can be easily understood by stakeholders with minimum or no technical background (Kelly et al., 2013). Due to these features, SD-based models have been used for facilitating public involvement in decision-making process, increasing public understanding of complex problems (Mirchi et al., 2012), and engaging stakeholders in the policy assessment process (e.g., Malard et al., 2017; Halbe et al., 2018). Despite their individual practicality and value, Q-methodology and SD have been applied separately in the literature and have not been combined for water quality modeling. This study proposes a framework by merging these existing methodologies to understand and incorporate stakeholders' viewpoints into water quality modeling toward the greater goal of improving water quality in agricultural areas. The framework was applied to the Qu'Appelle River Basin (QARB), Saskatchewan, Canada. The following section provides an overview of the QARB and the existing water quality model in the basin. This is followed by our proposed framework and demonstrated application. The paper concludes with a discussion of the implication for water quality management and directions for further research.

Fig. 1. The tributaries in the QARB, which were considered in this study.

Mean annual precipitation and temperature in the QARB are approximately 420 mm and 2.8 °C, respectively (Environment and Climate Change Canada, 2018). The QARB includes 12 tributaries (Fig. 1), from which the Moose Jaw River, Wascana Creek and Last Mountain Creek have the largest gross drainage areas (Table 1). The Qu'Appelle River is the main watercourse in the basin, which has the mean annual discharge of 6.5 m3/s before its confluence with the Assiniboine River in Manitoba (Pomeroy et al., 2005). The QARB supplies water to almost one third of the population in the province, and supports environmental, agricultural and industrial water demands as well as recreational usage (Hassanzadeh et al., 2014). Water quality in the QARB is under pressure, mainly due to nutrient pollution entering the river system from major cities and agricultural areas (Hall et al., 1999; Dixit et al., 2000). Most importantly, agriculture sector uses a significant portion of the land (Hassanzadeh et al., 2017). The dynamics of agricultural impacts on water quality are complex and stem from decisions of annual croppers and cattle producers. These producers have somewhat dissimilar values, and attitudes towards water quality in the system. While some farmers engage in both annual cropping and cattle production (mixed farmers), these two production systems have very different mechanisms of potential impact on water quality. In addition to practices to increase crop yield (e.g., fertilizer application), annual croppers in this region have drained

Table 1 The twelve tributaries in the QARB and their relevant information, obtained from Roste and Baulch (2017).

2. Study area and current water quality model 2.1. The Qu'Appelle River Basin The QARB, 52,000 km2, is situated mostly in the province of Saskatchewan, Canada (see Fig. S1 in the Supplementary material). 1118

Tributary

Drainage area (km2)

Wetland coverage area (%)

Animal population

Animal population as Nutrient Units

Ekapo Creek Indian Head Creek Iskwao Creek Jumping Deer Last Mountain Creek Loon Creek Moose Jaw River Pearl Creek Pheasant Creek Red Fox Creek Ridge Creek Wascana Creek

1154 260

4.9 1.5

5325 1030

2541 504

694 1714 12,711

7 2.4 8.75

2560 2800 24,130

1258 1100 11,659

2431 9425 1017 1394 2108 459 3865

2.6 2.9 3 2.6 4.25 2.6 2.45

3000 33,525 3000 2100 4170 2170 9440

1062 16,192 1062 1012 2021 1021 4557

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

tributaries over the period 1970–2014. The required data were mostly obtained from Water Security Agency (WSA, 2018). For calibration of SPARROW, the estimated TN and TP were compared with the corresponding observed values at the outlet of tributaries. The model performance was acceptable: R2 about 0.95 for both TN and TP as well as RMSE (Ton/year) equal to 0.46 and 0.56 for TN and TP, respectively. The statistically significant (p < 0.05) variables are related to sources of nutrients (fertilizer, manure, population and forest, among which fertilizer and forest accounts for the largest 92% and smallest 0.0013% impact on nutrient pollution), climate and land (precipitation, temperature, catchment slope, soil permeability), as well as aquatic delivery (streamflow discharge, length, velocity, reservoir hydraulics). Despite an acceptable model performance, estimated nutrient exports by SPARROW are uncertain. Most importantly, the required data at the sub-catchment scale are either unavailable or sparse; therefore, many assumptions were made about the calculation of input values to the model. Furthermore, SPARROW is a steady-state model that estimates long-term annual averages of loads, which means that simulation uncertainties may stem from ignoring the temporal variability of variables (e.g., climate) at finer scales. The level of uncertainty is reflected in the high errors (∼50%) for the long-term annual average nutrient flux estimates, nonetheless this error value is acceptable in the SPARROW literature (e.g., Schwarz et al., 2006). Despite model limitations, SPARROW provides results that can support management plans to mitigate nutrient export at the catchment-scale (Christianson et al., 2018; McLellan et al., 2018). Having said that, it is challenging to use SPARROW directly for stakeholder engagement in BMP evaluation. In brief, it is not easy to add new components to SPARROW that reflect the details and values that underpin the impact of BMPs in improving water quality. Furthermore, SPARROW is complex to be easily understood and used by stakeholders, in the absence of technical training. The model also does not instantly provide results such that stakeholders can explore different BMP options and discuss implementation issues or opportunities. For these reasons, we aimed to emulate and revise SPARROW in a more accessible environment for stakeholder engagement purposes.

wetlands over the past decades to increase efficiencies and profitability in crop production (McRae, 2013). Wetland drainage may contribute to increased erosion and sedimentation (e.g., Pomeroy et al., 2010; Yang et al., 2010), high concentrations of nutrients in runoff (Brunet and Westbrook, 2012; Armstrong, 2018), increased nitrification and phosphorus desorption over time (Brown et al., 2017), and higher flows, at least during wet periods (Dumanski et al., 2015; Ehsanzadeh et al., 2016). Cattle production in this region seldom uses mineral fertilizer or engages in wetland drainage. However, cattle producers often winter feed cattle in confined enclosures (corrals) which are sometimes located near rivers. This practice can increase nutrients in the feeding sites from manure, urines and residual feed, resulting in increased nutrient export to waterways during spring snowmelt (Smith, 2011; Smith et al., 2011; Chen et al., 2017). According to the Saskatchewan Watershed Authority (2003): “There are over 80,000 kilometers of stream course in agricultural Saskatchewan, along which many cattle operations are situated.” Given the combined impacts of cattle and grain farms in the landscape, improving water quality requires adoption of a series of BMPs suited to each type of farm. In addition, although cottage owners are expected to have minor impacts on water quality, their socio-economic activities are affected by the quality of water in the lakes and rivers. For instance, issues associated with eutrophication and cyanobacterial blooms, which are relatively common in the QARB lakes (Kehoe et al., 2015), can limit activities such as swimming, boating and fishing, and are known to have a significant negative effect on property values (Dodds et al., 2008). Therefore, it is critical to engage cottage owners in water quality decision-making processes to find workable solutions for this region that meet needs of different stakeholder groups. 2.2. Existing water quality model for the Qu'Appelle River Basin Water quality in the QARB has been simulated by Dr. Luis Morales, University of Saskatchewan using SPARROW (SPAtially Referenced Regression On Watershed; Schwarz et al., 2006). SPARROW is a statistical–based model developed by the USGS (United States Geological Survey, 2018) that relates water quality records with land-scape variables to estimate annual average nutrient budgets in large-scale catchments. The model is calibrated using observed annual nutrient loads estimated at stations located at the downstream end of stream reaches. SPARROW estimates annual averaged incremental loads at each river network reach and associated sub-catchment i as a nonlinear function of point and non-point nutrient sources and attenuations associated with landscape and aquatic processes (Equation (1)).

⎛⎛ ⎞ ⎛ ⎞ Nutrientloadi = ⎜ ⎜∑ aj Si, j ⎟ × exp ⎜ ∑ bj Li, j ⎟ + ⎠ ⎝ i ⎠ ⎝⎝ i

3. Framework design and implementation Our framework for stakeholder engagement in water quality modeling and BMP assessment in the QARB consisted of 7 stages (see Fig. 2). First, a series of small workshops were held with QARB stakeholders, namely agricultural producers, cottage owners, and water managers, to identify a list of potential agricultural BMPs for improving water quality in agricultural areas. Second, a series of more formal workshops followed, whereby agricultural producers and cottage owners were asked to sort BMPs based on their perceived effectiveness. Third, Q-methodology was used to identify common and dissenting viewpoints among stakeholders. Forth, a water quality model was developed using SD approach to simulate the nutrient loads in the region before and after implementation of stakeholder identified BMPs. Fifth, the water quality model was presented to agricultural producers and cottage owners, so they could interact with the model and observe the impact of their preferred BMPs on water quality in the basin. Sixth, a survey was then administered to gather feedback from stakeholders about the developed model, and potential barriers and recommended policies for adoption of BMPs. Finally, water managers will be provided with agricultural producers' viewpoints, including their preferred BMPs and policies, and a means to formulate options for improved water quality that are more likely to be adopted and implemented by land owners.

∑ cj Ni,j⎞⎟ i

⎠

⎛ −0.5dj Fi, j ⎞

× exp ⎜ ⎝

Vi , j

1 ⎛ ⎞ ⎟ × ⎜ −1 ⎟ 1 e + j Qi, j ⎠ ⎠ ⎝

(1)

where Si, j and Ni, j are non-point and point source of nutrients, respectively. Li, j is related to land-to-water transportation, and Fi, j , Vi, j , Qi, j are related to aquatic transport and respectively are streamflow length, velocity, and reservoir hydraulic loads. The aj , bj , cj , dj , and ej are calibration parameters. SPARROW has been successfully applied at the catchment scale (e.g., Morales-Marín et al., 2015; Ator and Garcia, 2016; Morales-Marín et al., 2017) and at various national and international scales (e.g., Hoos and McMahon, 2009; Benoy et al., 2016) to evaluate nutrient exports. The QARB encompasses a river network covering 14,779 reaches (i.e., 14,779 sub-catchments), from which Indian Head Creek has the lowest (81) and Moose Jaw River has the highest (3,408) number of sub-catchments. The SPARROW model estimates long-term annual average Nitrogen (N) and Phosphorus (P) loads for the sub-catchments, as well as Total N (TN) and Total P (TP) at the outlet of the QARB

3.1. Application of Q-methodology to identify stakeholders' preferred BMPs Q-methodology is an approach to extract similar patterns of viewpoints about a specific topic, whereby a range of statements related to the topic of interest is initially obtained through interviews with 1119

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

Fig. 2. Proposed framework for stakeholder engagement in water quality modeling.

stakeholders. Participants then engage in a “Q-sort” exercise to sort their level of agreement with statements by using a series of “score sheets” (Brown, 2004). Based on the analysis of score sheets, participants that have similar viewpoints can be identified (Lynch et al., 2014). Since the method represents the collection of thoughts rather than the people, a large number of participants is not necessarily required (Watts and Stenner, 2012). Q-methodology was used to understand stakeholders' viewpoints in the QARB with respect to effective agricultural BMPs about two scenarios: (1) a landowner is a cattle producer and has a set of corrals near a creek and the runoff from the corrals goes into the creek; and (2) a landowner is an annual cropper and drains wetlands and sends nutrients downstream that would have otherwise remained in the wetland. In January 2017, workshops were held with various groups of stakeholders in the QARB including water managers and policy-makers from Ministry of Agriculture (MoA, 2018), WSA, as well as agricultural producers and cottage owners, organized with support of the Moose Jaw River Stewards (MJRS, 2018) and Wascana Upper Qu'Appelle Watersheds (WUQWATR, 2018). The objective was to understand what a diverse group of stakeholders think about BMPs for nutrient control. An initial 100 statements were identified, that could be distilled to 32 statements that reflect an equal number of BMPs related to grain and cattle farms (see Table 2 for the list of Q-sorts). Two workshops were then held in March 2017 to understand stakeholders' viewpoints about the 32 BMPs and their impact on water quality. There were 28 workshop participants, the majority of whom were agricultural producers and the remainder were cottage owners. Participants were provided with 32 cards, each containing a BMP practice and its associated number, as well as a score sheet (see Fig. S2 in the Supplementary materials). Participants then sorted the cards according to the extent they agreed/disagreed with the effectiveness of each BMPs. The scores obtained from these groups were separately included in the PQmethod software (Schmolck, 2002), which is commonly used for Q-analysis (e.g., Alexander et al., 2018). Accordingly, factor analysis was performed to narrow the many individual viewpoints into few factors that represent shared ways of thinking among participants. After examining various factors, three and two factor solutions respectively were found for agricultural producers and cottage owners. To compare the statement factors, z-scores were calculated for each of statements under the identified factor solutions. The highest and lowest z-scores respectively indicate the highest and lowest acceptability of a statement among participants that fell into that factor solution (Brown, 2005). Table 2 shows the factor solutions and their associated z-scores for stakeholder identified BMP statements. Factor analysis for agricultural producers: Agricultural producers had three distinct viewpoints about BMPs that can improve water quality. Considering the high z-score values (z-score > 1) for these factors, agricultural producers think the most effective BMPs are mainly related to (1) flow and erosion control, (2) cattle feeding and site control, as

well as (3) mixed wetland and cattle site management. The agricultural producers score sheets were further investigated to understand whether annual croppers, livestock producers or mixed farmers have different perspectives. While there were some differences in viewpoints, all types of agricultural producers identified BMPs related to wetland retention/ restoration and flow and erosion control as the best ways to improve water quality in the QARB. Most farmers identified the relocation of corrals away from creeks as the best way to reduce corral impacts. All types of agricultural producers disagreed on leaving drainage ‘as is’ and keeping current corrals near waterways until they will be forced to change current practices by regulators. This consensus view indicates that farmers agree on voluntarily changing current conditions to improve water quality. As agricultural producers were asked to only reflect on the impact of drainage on water quality, we assumed that they did not consider other impact of drainage e.g., on flood risk in their BMP consideration. Among different types of agricultural producers, annual croppers viewed flow and erosion control as more effective than fertilizer management practices. Among livestock producers and mixed farmers, there was a divergence in the approach to addressing corrals. Some farmers favored “hard” construction-type solutions, such as the relocation of corrals and construction of runoff control ponds below corrals, while others preferred “soft” management-oriented solutions, such as moving cattle to feed extensively and dispensing with corrals altogether. Factor analysis for cottage owners: Cottage owners' distinct viewpoints about BMPs were related to (1) mixed wetland and cattle site management and (2) mixed fertilizer and cattle site management. High z-scores in Table 2 imply that, like the agricultural producers, cottage owners placed high priority on wetland restoration and the relocation of livestock facilities away from creeks. In contrast to agricultural producers, cottage owners think that fertilizer management is an important practice to improve water quality. Similar to agricultural producers, cottage owners strongly disagreed with leaving drainage as is and keeping corrals near the creek until regulators force these practices to change (see the lowest z-scores, highlighted in bold in Table 2). In addition to understanding different viewpoints, the BMPs identified as the most effective (with z-score > 1) were collected based on all groups of agricultural producers and cottage owners to quantitatively assess their impact on water quality in the region. These BMPs are summarized in Table 3. The top four are related to cattle farming near creeks and the rest are related to annual cropping when land is drained. 3.2. Emulation of the water quality model An SD environment, Vensim DSS software (Ventana Systems, 2018), was used to develop a transparent, user-friendly, and accessible water quality model for the QARB, namely the SD-Qu'Appelle. The model was developed by emulating the SPARROW for the basin. Calibration parameters and input data for each of the sub-catchments were 1120

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

Table 2 Factor solutions and z-scores for Q-sort statements by stakeholders. Bold cells indicate the most favorable BMPs. No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

BMP statements

Agriculture producers

Leave drainage works as is until forced to change practices by regulators Restoring wetlands to reduce drainage impacts Draining smaller wetlands into larger wetlands instead of draining them off the land Retaining wetlands to reduce drainage impacts Use slow release fertilizer to reduce water quality impacts when land is drained Minimum or zero tillage practices to reduce water quality impacts when land is drained Auto-steer technology to reduce fertilizer over application when land is drained Variable rate fertilizer application when land is drained Seeding ditches to perennial grass for erosion control Side slope ditches for erosion control Installing small storage dams for retaining water Installing flow controls for erosion control Converting small targeted portions cropland to perennial forage to avoid having to drain lands Converting entire fields cropland to perennial forage to avoid having to drain land Growing winter cereals to reduce erosion Incorporating perennial forage into crop rotation Split fertilizer application to reduce water quality impacts when land is drained Reduce fertilizer application to reduce water quality impacts when land is drained Feed cattle extensively in winter away from waterways using portable windbreaks Feed cattle extensively in winter away from waterways using alternate watering points Complete relocation of cattle corrals away from creeks Feed cattle extensively in winter away from waterways by bale grazing Move to a later calving date to avoid using corrals which drain into a waterway Construct holding ponds below corrals to control runoff from entering waterways Minimize the time animals spend in corrals near creeks by using them calve few animals at a time Feed cattle extensively in winter away from waterways using swath grazing or grazing corn Feed cattle extensively in the winter away from waterways with a tractor or bale processor Have livestock custom wintered at locations which do not drain into a waterway Work with regulators to bring corrals into compliance with regulations Converting operation to a grasser or custom grazing to avoid using corrals Reduce/eliminate the cattle component of operation so that corrals near waterways are not used Keep current corrals near waterway as is until forced to change by regulators

Cottage owners

Factor 1

Factor 2

Factor 3

Factor 1

Factor 2

−2.22 0.58 0.19 1.75 −0.04 −0.04 0.25 0.32 1.52 1.10 1.37 1.24 −0.44 −0.52 −0.73 −0.06 −0.31 −0.62 0.04 −0.12 1.39 −0.21 −1.11 1.31 −1.02 −0.13 −0.12 −0.06 0.96 −0.66 −1.54 −2.08

−1.94 −0.19 −0.66 −0.19 0.16 0.30 −0.11 −0.01 1.03 −0.62 −0.55 −0.69 −0.25 −0.41 0.04 0.39 0.02 −0.33 0.97 1.41 0.80 1.91 0.64 1.66 −0.38 2.09 0.60 −1.29 −0.18 −0.64 −1.55 −2.02

−2.05 1.95 −0.31 1.29 −1.39 −0.19 −0.44 0.18 −0.62 −1.14 −0.29 −0.69 0.42 0.95 −0.66 −0.42 −0.54 −1.07 0.67 0.41 1.99 0.49 0.21 0.88 0.91 −0.04 0.11 0.96 0.33 −0.18 0.67 −2.38

−1.48 2.18 0.78 2.38 0.41 0.56 −0.09 0.41 −0.06 −0.72 −0.72 −0.60 −0.35 −0.20 −0.36 0.24 −0.85 0.01 0.43 0.65 1.24 −0.25 −0.96 −1.97 −0.27 −0.43 −0.51 1.31 0.64 0.48 0.25 −2.18

−1.88 1.25 0.61 0.66 1.27 0.00 −0.63 1.83 0.00 −0.59 −0.02 0.02 −1.22 0.00 −0.63 −0.63 −0.61 1.24 −1.86 0.64 1.88 0.64 −1.25 1.24 −1.27 0.02 0.59 0.63 −0.63 −0.02 −1.25 −0.03

right). By clicking on each of the model components, a user can find the data series associated with sub-catchments in the Moose Jaw River tributary. The visual comparison and R2 values in Figs. S3 and S4 in the Supplementary materials indicate that the SD-Qu'Appelle successfully emulates SPARROW for the estimation of incremental N and P loads in all tributaries. Moreover, Fig. S5 in the Supplementary materials show that TN and TP (Ton/year) at the outlet of the tributaries based on SDQu'Appelle fairly represented SPARROW's estimated total loads.

extracted from SPARROW. Equation (1) (above) was used to calculate the annual average incremental N and P loads (Ton/year) for the subcatchments within tributaries. The sub-catchments nutrient loads were summed up to represent the TN and TP loads (Ton/year) at the outlet of the tributaries. To avoid complexity in representing processes, instead of building individual sub-catchment modules and connecting them to spatially represent the movement of loads in the tributaries, a single water quality model was developed for each tributary and sub-catchment information was incorporated into the model as data series. Therefore, the model is unable to represent nutrient concentrations, which are dependent on the location of the sub-catchments and the contributions from the drainage areas of upstream reaches. Fig. 3 shows the structure of the SD-Qu'Appelle for estimating incremental N and TN loads in the Moose Jaw River tributary. Variables and coefficients in the model represent the point and non-point sources (left), climate and land properties (top-center), as well as streamflow/reservoir processes (top-

3.3. Addition of pressures and buffers to the SD-Qu'Appelle To use the SD-Qu'Appelle for BMP impact assessment, we added two components related to water quality pressures and buffers in this region: the existence of corrals near creeks, and wetlands. To include corrals near creeks in the QARB to the SD-Qu'Appelle, the total number of animals and their nutrient waste production, was obtained from Roste and Baulch (2017). As information about the exact location of the

Table 3 The most effective BMPs identified by agricultural producers and cottage owners for the considered scenarios. Scenarios

Most effective BMPs identified by stakeholders

A landowner is a cattle producer and has a set of corrals near a creek and the runoff from the pens goes into the creek

Construct holding pounds below creeks Complete relocation of cattle corrals Feed cattle extensively away from creek Have livestock custom wintered at locations that does not drain into creek Wetland restoration and retaining Slow release fertilizer Variable rate fertilizer Install flow controls Install storage dames Seeding ditches

A landowner is an annual cropper and drains wetlands and sends nutrients downstream that would have otherwise remained in the wetland

1121

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

Fig. 3. Schematic of the SD-Qu'Appelle to calculate the incremental and total N loads based on point and non-point source pollution (left), the characteristics of land/ climate (top-center), and streamflow/reservoirs (right) for the Moose Jaw River tributary in the QARB. Similar model structure was developed to estimate incremental and total N and P loads in the rest of tributaries.

the incremental and total loads before and after implementation of BMPs. These coefficients were obtained based on the literature and/or discussion with the WSA and are explained below. Based on the literature, variable rate fertilizer application and slow release fertilizer can reduce the amount of nutrients entering the land on average by 3.5% (Roste and Baulch, 2017) and 4.2% (Haderlein et al., 2001), respectively. Accordingly, these percentages for fertilizer N and P reduction due to the implementation of BMPs were incorporated in the calculation of fertilizer amount in the SD-Qu'Appelle. If these BMPs are selected, fertilizer N and P amounts will be reduced accordingly, and the model will calculate the incremental and total nutrient loads based on these reductions in fertilizer amount. Nonetheless, estimated impact of these BMPs on improving water quality includes large uncertainty. Implementation of these BMPs might not have an immediate impact on nutrient loads in the region, for example due to the potential existence of legacy nutrients in the landscape (Potter et al., 2010; Van Meter et al., 2016). For all BMPs related to corrals, either soft or hard management solutions, it was assumed that if they are implemented correctly they will fully reduce nutrient amounts entering creeks. For instance, one BMP is related to the movement of corrals to noncontributing areas. These are areas where the land does not contribute to streamflow in an average year, hence nutrients would not be transported from these soils, given groundwater is not a significant contributor to streamflow in this semi-arid region (Pomeroy et al., 2005). Functionally, this model assumes the removal of a critical source area by removing the hydrologic connectivity of a major nutrient source; however, a lingering research question is the potential for episodic exports during extreme wet periods, which is not considered here. For wetland restoration, since the current percentage of wetland coverage is incorporated in the model, one can easily change this percentage and see the impact on nutrient loads in each tributary. For considering the impact of wetland restoration, it was assumed that the percentage of existing wetland coverage area will be increased by 13% in all tributaries. This number was simply chosen to compare the results with an existing report on BMP impacts in the region (i.e., Roste and Baulch, 2017). Similarly, one can drain wetlands by reducing the current wetland coverage percentage to observe the impact of wetland drainage on nutrient loads. The impact of BMPs related to flow control and storage dams on nutrient loads depends on the size, shape, number and location of the corresponding hydraulic structures. Therefore, understanding the impact of these BMPs requires long-term field experiments, and unfortunately data is lacking for this catchment. One option is to use literature data on the impact of BMPs in the regions that have similar

cattle farms in the QARB was unavailable, it was assumed that all animals stay in corrals near creeks (i.e., for water supply, although this represents an upper bound). Based on the total number of animals, and types of animals for each tributary, the corresponding Nutrient Unit (NU) was calculated (similar to Roste and Baulch, 2017). The NU is “the number of animals that will produce the amount of nutrients that give the fertilizer replacement value of the lower of 43 kilograms of N or 55 kilograms of P as nutrient” (Ontario Ministry of Agriculture Food and Rural Affairs, 2007). The total amounts of animal N (55 kgr × UN) and P (45 kgr × UN) waste produced in kilograms was then calculated for each tributary (Roste and Baulch, 2017). Because the exact number of animals per sub-catchments within the tributaries was unknown, it was assumed that the total amounts of animal N and P is distributed among sub-catchments in the tributaries based on the ratio of sub-catchment area over tributary area. The numbers of animals, relationship to transfer number of animals to T and P Nutrient Units, as well as the ratio of sub-catchment area over tributary area were incorporated into the model to represent animal N and P values corresponding to corrals near creeks in each sub-catchment, which is treated as another nonpoint source of pollution entering the land. To incorporate wetlands in the model, the total percentage of wetland coverage in the QARB tributaries was obtained from Roste and Baulch (2017). Similar to corrals, since percentage wetland coverage in each sub-catchment is unknown, it was assumed that wetland coverage in sub-catchments is equal to the total percentage of wetland coverage multiplied by the ratio of sub-catchment area over tributary area. The wetland coverage percentage for each sub-catchment was included in the SD-Qu'Appelle, where it affects the land-to-water delivery calculations. Since the SPARROW estimations represent gauged values (see Section 2), to ensure that the modified SD-Qu'Appelle can still fairly represent the observations, the model was recalibrated against SPARROW to re-estimate calibration coefficients for the newly added and existing model variables (i.e. corrals and wetlands). One of the advantages of this model is that one can change the number of animals and/or percentage of wetland coverage in SD-Qu'Appelle and see the impact of these changes in nutrient loads in sub-catchments and tributaries.

3.4. Inclusion of BMPs in the model The identified BMPs in Table 3 were represented in the SD-Qu'Appelle using coefficients that affect the magnitude of fertilizer, animal manure near creek, wetlands, as well as streamflow/reservoirs. Fig. 4 shows the schematic of the SD-Qu'Appelle after incorporation of BMPs (shown in red) in the model; therefore, the model is able to calculate 1122

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

Fig. 4. Schematic of the SD-Qu'Appelle after implementation of BMPs (shown in red) to calculate the incremental and total N loads for Moose Jaw River tributary. Similar model structure was developed to calculate the incremental and total N and P loads after BMP implementation for the rest of tributaries. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

3.5. Model graphical user interface

characteristics as the QARB (e.g., snow-dominated catchments). Based on the literature from the Northern Great Plains, storage dams were assumed to reduce nutrient loads by 15% (e.g., Tiessen et al., 2011). Literature of the efficacy of flow controls to reduce nutrients is lacking however installation of such structures is expected to be become a common practice to abate flooding impacts. The nutrient loading abatement potential of flow controls is through reducing velocities and peak discharge downstream. For the purpose of this study, the flow controls were assumed to reduce nutrient loads by 3.5% (Etienne Shupena-Soulodre pers. Comm). Based on our knowledge, the impact of grassing ditches and waterways on improving water quality in areas with similar characteristics to the QARB has not been studied in the literature. However, this BMP has been implemented within the QARB in some regions in the past and water quality measurements have revealed that seeding ditches can improve water quality at least in short-term, due to slowing water flows, and increasing sedimentation. As a result, it was assumed that this BMP can reduce nutrient loads by 10%. This assumption is crude and further investigation is required to understand the impact of this BMP on nutrient loads, particularly given evidence that vegetative residues can be important nutrient sources (Elliott, 2013). Fig. 5 here and Fig. S6 in the Supplementary materials show the potential for reduction in TN and TP (as a % of reference loads) due to implementation of the selected BMPs in the Qu'Appelle tributaries, respectively. The modelled impact of BMPs on reducing nutrients depends on the characteristics of the tributaries. Overall, BMPs related to corrals near creeks, wetland restoration, installing storage dams and seeding ditches have the highest predicted impact on nutrient reduction in the QARB. However, the estimated BMP impacts on nutrient loads are uncertain due to uncertainties in the spatial distribution of nutrient sources, variable or unknown efficacy of BMPs in snow-dominated climates, modeling assumptions, and challenges in understanding how quickly these benefits may occur. The major goal of our study, however, was to develop a platform for stakeholders to discuss their viewpoints, rather than providing specific water quality reduction estimates. We communicated these uncertainties with stakeholders and noted that the BMP-related coefficients can be easily revised in the model as further information becomes available. Overall, we suggest that in data-sparse regions such as this, adaptive management, where monitoring is performed, and management plans revised through time, is a prudent way forward, with insights here helping inform priorities for BMP implementation and barriers to effective policy change.

Fig. 6 shows the constructed Graphical User Interface (GUI) for the SD-Qu'Appelle to simulate the single and joint impact of BMPs on TN loads in the Qu'Appelle tributaries. A similar GUI was developed to present BMP impacts on TP. The BMPs related to corrals near creeks as well as annual cropping when land is drained are shown in the left. By scrolling on any of horizontal bars below these BMPs from zero to one, their impact is simulated in the model and accordingly, the TN loads (Ton/year) for the Qu'Appelle tributaries are shown in right for before and after implementation of BMPs. For instance, in Fig. 6, the flow control BMP was selected and its impact on reducing TN in the QARB tributaries was instantly shown (blue bars). 3.6. Real-time stakeholder engagement in simulations We held two workshops in November 2017 to allow participants to simulate the SD-Qu'Appelle and instantly see the potential consequences of their preferred BMPs on water quality and to discuss their viewpoints. A brief presentation was given to introduce the water quality model: model development and BMPs incorporation procedures, model functionality, and uncertainty in the results. Participants then explored the model to see the impact of one or a combination of BMPs on water quality in the QARB tributary. Participants were able to examine the model components and customize information such as the amount of fertilizer and number of cattle and then see the impact on nutrient loads to understand the effectiveness of the BMP. During this exercise, participants completed a survey about the BMPs, model's GUI, and the value of the model for water quality management. A summary of the survey results is presented in Table 4. Participants were supportive of the SD-Qu'Appelle and acknowledged the benefits of the model for long-term water quality management. In particular, they recommended using the model for engaging younger farmers in water quality analysis as there would be a high chance that younger, rather than older, farmers change their land practices to adopt BMPs. Participants also asked for a more finer-scale model, so that they can pinpoint their individual farms in the model and add their own on-farm information for BMP impact assessment. This is a valuable suggestion; however, all farmers might not agree with that as they might not be comfortable to reveal how much nutrient they produce. Participants also noted that the model estimates the nutrient loads under either a ‘no’ or ‘total implementation’ scenario for BMPs, whereas in practice BMPs are often partially implemented on land, which should be reflected in future analysis. Regarding policy decisions, participants were 1123

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

Fig. 5. Percentage of relative reduction in TN in the Qu'Appelle tributaries after implementation of the BMPs (%).

Fig. 6. The GUI for the SD-Qu'Appelle. The stakeholder identified BMPs are shown in left. The horizontal bars below BMPs show whether the BMPs is implemented or not by 1 and 0, respectively. The panels in right represent the TN (Ton/year) for QARB tributaries before and after implementation of BMPs by red and blue bars, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

implementation of BMPs on their land. This study proposed a framework to integrate agricultural producers' values in water quality modeling, and applied the framework to assess BMPs for the Qu'Appelle River Basin, Saskatchewan. Using Q-methodology a set of effective BMPs that most stakeholders agreed with were identified and integrated to a simple SD-based water quality model, SD-Qu'Appelle, to simulate the nutrient loads in the basin before and after implementation of the BMPs. SD-Qu'Appelle's performance in estimating nutrient loads was verified by comparing it to an existing water quality model for the basin. The SD-Qu'Appelle was used for stakeholder engagement in simulations, thus providing a venue for recommendations about the BMPs, barriers to their implementation, and policy preferences to support implementation. Overall, stakeholders acknowledged the importance of such engagement for water quality management and the benefits of the model for supporting BMP discussions and identifying collaborative water quality management solutions. Stakeholders

asked to sort the key barriers i.e., cost, time, and no obvious benefits of BMP. Results indicate that economics is a principal driver that affect agricultural producers' decisions, followed by time requirements to implement, and no obvious benefit of the BMP. Agricultural producers recommended the inclusion of cost-benefit analyses in the SD-Qu'Appelle to increase the value of the model for land owner decisionmaking. They also indicated that incentives rather than regulation can increase the adoption of BMPs, and ultimately lead to improved water quality in the QARB. 4. Conclusions Agricultural producers play a key role in adopting agricultural BMPs that can improve water quality; however, they often have different ranges of viewpoints and perspectives about land management and water quality which influence their decision to accept or refuse the 1124

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

Table 4 Participants' feedbacks and recommendations regarding the BMPs, water quality model, GUI, and the value of model. Performance Indicator

Participants feedbacks

Participants recommandations

BMPs

-Agreed with the importance of the selected BMPs. -Some noted “Variable-rate fertilizer” is an uncommon strategy in the region. -Wetland “retention” rather than “restoration” is more realistic.

- Remove “variable-rate fertilizer” from the BMP options. - Change “restore” to “retain” for wetlands.

Model structure

- Noted the spatial scale of the model is broad.

Model GUI

- Expressed curiosity about the cumulative effects of BMPs on in-stream nutrient reduction. - Noted that BMPs are partially implemented in the real world, but the model presents them as ‘on’ or ‘off’ switches.

Model value to Stakeholders

- Noted the model is a great tool for long-term planning. - Noted the model is a positive engagement tool to discuss BMP impacts.

- Personalize the model: e.g., clearly identify where individual farms are. -Add a midpoint to slider bars to represent partial BMP implementation. - Present the percentage of change in nutrients due to BMP impact rather than change in nutrient loads. - Display the cumulative effects of BMPs on in-stream water quality. - Include cost-benefit analyses. - Couple the model with existing sustainability programs. -Target younger audiences.

suggested improving the accuracy of SD-Qu'Appelle to support more informed decisions about BMP adoption, and the need for a cost-benefit component – since economic factors was identified as one of the most important factors when deciding whether or not to implement a BMP. Our next step is to understand the real impacts of BMPs in the region using field experiments, and to incorporate stakeholders' suggestions for improvement in SD-Qu'Appelle to support improved policy analysis. In conclusion, we suggest that combining Q-methodology and SD approach can be a valuable way of understanding and incorporating stakeholders' viewpoints into water quality modeling. This type of stakeholder engagement in water quality analysis can increase the chance of BMP uptake and can lead to improve water quality at local and regional scales.

561–567. Brown, M., 2004. Illuminating Patterns of Perception: an Overview of Q Methodology (No. CMU/SEI-2004-TN-026). Carnegie-Mellon Univ Pittsburgh PA Software Engineering Institute. Brown, S.R., 2005. Applying Q Methodology to Empowerment. Measuring Empowerment: Cross-disciplinary Perspectives. The World Bank. Brown, R., Zhang, Z., Comeau, L.P., Bedard-Haughn, A., 2017. Effects of drainage duration on mineral wetland soils in a Prairie Pothole agroecosystem. Soil Res. 168, 187–197. Brunet, N.N., Westbrook, C.J., 2012. Wetland drainage in the Canadian prairies: nutrient, salt and bacteria characteristics. Agric. Ecosyst. Environ. 146, 1–12. Bumbudsanpharoke, W., Moran, D., Hall, C., 2009. Exploring perspectives of environmental best management practices in Thai agriculture: an application of Q-methodology. Environ. Conserv. 36, 225–234. Butler, C., Adamowski, J., 2015. Empowering marginalized communities in water resources management: addressing inequitable practices in Participatory Model Building. J. Environ. Manag. 153, 153–162. Chapra, S.C., 2008. Surface Water-quality Modeling. Waveland Press. Chen, G., Elliott, J.A., Lobb, D.A., Flaten, D.N., Braul, L., Wilson, H.F., 2017. Changes in runoff chemistry and soil fertility after multiple years of cattle winter bale feeding on annual cropland on the Canadian prairies. Agric. Ecosyst. Environ. 240, 1–13. Christianson, R., Christianson, L., Wong, C., Helmers, M., McIsaac, G., Mulla, D., McDonald, M., 2018. Beyond the nutrient strategies: common ground to accelerate agricultural water quality improvement in the upper Midwest. J. Environ. Manag. 206, 1072–1080. Cousins, J.J., 2017. Infrastructure and institutions: stakeholder perspectives of stormwater governance in Chicago. Cities 66, 44–52. Cuppen, E., Breukers, S., Hisschemöller, M., Bergsma, E., 2010. Q methodology to select participants for a stakeholder dialogue on energy options from biomass in The Netherlands. Ecol. Econ. 69, 579–591. Davies, B.B., Hodge, I.D., 2007. Exploring environmental perspectives in lowland agriculture: a Q methodology study in East Anglia, UK. Ecol. Econ. 61, 323–333. Dixit, A.S., Hall, R.I., Leavitt, P.R., Quinlan, R., Smol, J., 2000. Effects of sequential depositional basins on lake response to urban and agricultural pollution: a palaeoecological analysis of the Qu'Appelle Valley, Saskatchewan, Canada. Freshw. Biol. 43, 319–337. Dodds, W.K., Bouska, W.W., Eitzmann, J.L., Pilger, T.J., Pitts, K.L., Riley, A.J., Schloesser, J.T., Thornbrugh, D.J., 2008. Eutrophication of US freshwaters: analysis of potential economic damages. Environ. Sci. Technol. 43, 12–19. Dumanski, S., Pomeroy, J.W., Westbrook, C.J., 2015. Hydrological regime changes in a Canadian Prairie basin. Hydrol. Process. 29, 3893–3904. Ehsanzadeh, E., van der Kamp, G., Spence, C., 2016. On the changes in long-term streamflow regimes in the North American Prairies. Hydrol. Sci. J. 61, 64–78. Elliott, J., 2013. Evaluating the potential contribution of vegetation as a nutrient source in snowmelt runoff. Can. J. Soil Sci. 93, 435–443. Environment and Climate Change Canada., 2018. https://www.canada.ca/en/ environment-climate-change.html. Forrester, J.W., 1961. Industrial Dynamics. MIT Press, Cambridge. M.A. Gastelum, J.R., Krishnamurthy, G., Ochoa, N., Sibbett, S., Armstrong, M., Kalaria, P., 2018. The use of system dynamics model to enhance integrated resources planning implementation. Water Resour. Manag. 32, 2247–2260. Guo, H.C., Liu, L., Huang, G.H., Fuller, G.A., Zou, R., Yin, Y.Y., 2001. A system dynamics approach for regional environmental planning and management: a study for the Lake Erhai Basin. J. Environ. Manag. 61, 93–111. Haderlein, L., Jensen, T.L., Dowbenko, R.E., Blaylock, A.D., 2001. Controlled release urea as a nitrogen source for spring wheat in western Canada: yield, grain N content, and N use efficiency. Sci. World J. 1, 114–121. Halbe, J., Pahl-Wostl, C., Sendzimir, J., Adamowski, J., 2013. Towards adaptive and integrated management paradigms to meet the challenges of water governance. Water Sci. Technol. 67, 2651–2660. Halbe, J., Pahl-Wostl, C., Adamowski, J., 2018. A methodological framework to support the initiation, design and institutionalization of participatory modeling processes in

Acknowledgements The authors are grateful to workshop participants, as well as Wascanna Upper Qu'Appelle Watersheds and Moose Jaw River Watershed Stewards for their support in arranging the workshops. The authors also acknowledge Ashleigh Duffy and Anuja Thapa for their support in policy analysis. This project was funded by the Environment and Climate Change Canada Environmental Damage Fund (4175441323-8000), as well as NSERC Create for Water Security (417003-10338000). This research project has been approved on ethical grounds by the University of Saskatchewan Behavioural Research Ethics Board. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jenvman.2018.11.016. References Agriculture and Agri-food Canada, 2000. Agricultural Best Management Practices. http://www5.agr.gc.ca/resources/prod/doc/pfra/pdf/Agric_bmp_e.pdf. Alexander, K.S., Parry, L., Thammavong, P., Sacklokham, S., Pasouvang, S., Connell, J.G., Jovanovic, T., Moglia, M., Larson, S., Case, P., 2018. Rice farming systems in Southern Lao PDR: interpreting farmers' agricultural production decisions using Q methodology. Agric. Syst. 160, 1–10. Armstrong, M., 2018. Water Quality Impacts of Wetland Drainage and Climate Change in the Prairie Pothole Region. Masters thesis. University of Saskatchewan, Saskatoon, pp. 111. Ator, S.W., Garcia, A.M., 2016. Application of SPARROW modeling to understanding contaminant fate and transport from uplands to streams. J. Am. Water Resour. Assoc. 52, 685–704. Baumgart-Getz, A., Prokopy, L.S., Floress, K., 2012. Why farmers adopt best management practice in the United States: a meta-analysis of the adoption literature. J. Environ. Manag. 96, 17–25. Benoy, G.A., Jenkinson, R.W., Robertson, D.M., Saad, D.A., 2016. Nutrient delivery to lake winnipeg from the red—Assiniboine River basin–A binational application of the SPARROW model. Can. Water Resour. J. 41, 429–447. Brown, S.R., 1996. Q methodology and qualitative research. Qual. Health Res. 6 (4),

1125

Journal of Environmental Management 231 (2019) 1117–1126

E. Hassanzadeh et al.

incentives that promote voluntary phosphorus abatement in agricultural watersheds. J. Soil Water Conserv. 72, 493–505. Pomeroy, J.W., de Boer, D., Martz, L., 2005. Hydrology and Water Resources of Saskatchewan. University of Saskatchewan, Saskatoon, Saskatchewan, Canada, pp. 25. Pomeroy, J., Fang, X., Westbrook, C., Minke, A., Guo, X., Brown, T., 2010. Prairie Hydrological Model Study Final Report. Centre for Hydrology Report No. 7 University of Saskatchewan, Saskatoon, Saskatchewan, pp. 113. Potter, P., Ramankutty, N., Bennett, E.M., Donner, S.D., 2010. Characterizing the spatial patterns of global fertilizer application and manure production. Earth Interact. 14, 1–22. Prokopy, L.S., Floress, K., Klotthor-Weinkauf, D., Baumgart-Getz, A., 2008. Determinants of agricultural best management practice adoption: evidence from the literature. J. Soil Water Conserv. 63, 300–311. Qiu, J., Shen, Z., Chen, L., Xie, H., Sun, C., Huang, Q., 2014. The stakeholder preference for best management practices in the three gorges reservoir region. Environ. Manag. 54, 1163–1174. Roste, J., Baulch, H., 2017. Lower Qu'Appelle Watershed, SK: Land Use and Water Quality. Unpublished report. pp. 1–33. Saskatchewan Watershed Authority, 2003. Stewardship and Economics of Cattle Wintering Sites. http://www1.foragebeef.ca/$foragebeef/frgebeef.nsf/all/ccf154/ $FILE/stewardshipandeconomicsofcattlewinteringsites.pdf. Schall, D., Lansing, D., Leisnham, P., Shirmohammadi, A., Montas, H., Hutson, T., 2018. Understanding stakeholder perspectives on agricultural best management practices and environmental change in the Chesapeake Bay: AQ methodology study. J. Rural Stud. 60, 21–31. Schmolck, P., 2002. PQMethod software. Available at: http://schmolck.userweb.mwn. de/qmethod/. Schwarz, G.E., Hoos, A.B., Alexander, R.B., Smith, R.A., 2006. The SPARROW Surface Water-quality Model: Theory, Application and User Documentation. https://pubs. usgs.gov/tm/2006/tm6b3/PDF/tm6b3_part1a.pdf. Simonovic, S.P., 2009. Managing Water Resources: Methods and Tools for a Systems Approach. UNESCO, Paris. Smith, A., 2011. Nutrient Export in Run-off from an In-field Cattle Overwintering Site in East-central Saskatchewan. Thesis for the Master of Science. Department of Soil Science. University of Saskatchewan, Saskatoon, Saskatchewan, Canada. Smith, A., Schoenau, J., Lardner, H.A., Elliott, J., 2011. Nutrient export in run-off from an in-field cattle overwintering site in East-Central Saskatchewan. Water Sci. Technol. 64, 1790–1795. Stang, C., Gharabaghi, B., Rudra, R., Golmohammadi, G., Mahboubi, A.A., Ahmed, S.I., 2016. Conservation management practices: success story of the hog creek and sturgeon river watersheds, Ontario, Canada. J. Soil Water Conserv. 71, 237–248. Steelman, T.A., Maguire, L.A., 1999. Understanding participant perspectives: Q-methodology in national forest management. J. Pol. Anal. Manag. 361–388. Strickert, G., Chun, K.P., Bradford, L., Clark, D., Gober, P., Reed, M.G., Payton, D., 2016. Unpacking viewpoints on water security: lessons from the south saskatchewan River Basin. Water Pol. 18, 50–72. Tiessen, K.H.D., Elliott, J.A., Stainton, M., Yarotski, J., Flaten, D.N., Lobb, D.A., 2011. The effectiveness of small-scale headwater storage dams and reservoirs on stream water quality and quantity in the Canadian Prairies. J. Soil Water Conserv. 66, 158–171. United States Geological Survey, 2018. https://www.usgs.gov/. Van Meter, K.J., Basu, N.B., Veenstra, J.J., Burras, C.L., 2016. The nitrogen legacy: emerging evidence of nitrogen accumulation in anthropogenic landscapes. Environ. Res. Lett. 11, 035014. Ventana Systems, 2018. http://www.vensim.com/store/index.php?route=product/ product&product_id=53. Wascana Upper Qu'Appelle Watersheds, 2018. http://wuqwatr.ca/. Water Security Agency, 2018. https://www.wsask.ca/. Watts, S., Stenner, P., 2012. Doing Q Methodological Research: Theory, Method & Interpretation. Sage, London. Webler, T., Danielson, S., Tuler, S., 2009. Using Q Method to Reveal Social Perspectives in Environmental Research. Social and Environmental Research Institute, Greenfield, MA. Winz, I., Brierley, G., Trowsdale, S., 2009. The use of system dynamics simulation in water resources management. Water Resour. Manag. 23, 1301–1323. Yang, W., Wang, X., Liu, Y., Gabor, S., Boychuk, L., Badiou, P., 2010. Simulated environmental effects of wetland restoration scenarios in a typical Canadian prairie watershed. Wetl. Ecol. Manag. 18, 269–279.

water resources management. J. Hydrol. 556, 701–716. Hall, R.I., Leavitt, P.R., Quinlan, R., Dixit, A.S., Smol, J.P., 1999. Effects of agriculture, urbanization, and climate on water quality in the northern Great Plains. Limnol. Oceanogr. 44, 739–756. Hassanzadeh, E., Zarghami, M., Hassanzadeh, Y., 2012. Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resour. Manag. 26, 129–145. Hassanzadeh, E., Elshorbagy, A., Wheater, H., Gober, P., 2014. Managing water in complex systems: an integrated water resources model for Saskatchewan, Canada. Environ. Model. Softw. 58, 12–26. Hassanzadeh, E., Elshorbagy, A., Wheater, H., Gober, P., Nazemi, A., 2015. Integrating supply uncertainties from stochastic modeling into integrated water resource management: case study of the Saskatchewan River basin. J. Water Resour. Plann. Manag. 142 (2), 05015006. Hassanzadeh, E., Elshorbagy, A., Nazemi, A., Jardine, T.D., Wheater, H., Lindenschmidt, K.E., 2017. The ecohydrological vulnerability of a large inland delta to changing regional streamflows and upstream irrigation expansion. Ecohydrology 10 (4), e1824. Hoos, A.B., McMahon, G., 2009. Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks. Hydrol. Process. 23, 2275–2294. Inam, A., Adamowski, J., Halbe, J., Prasher, S., 2015. Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: a case study in the Rechna Doab watershed, Pakistan. J. Environ. Manag. 152, 251–267. Ji, Z.G., 2017. Hydrodynamics and Water Quality: Modeling Rivers, Lakes, and Estuaries. John Wiley & Sons. Kehoe, M.J., Chun, K.P., Baulch, H.M., 2015. Who smells? Forecasting taste and odor in a drinking water reservoir. Environ. Sci. Technol. 49, 10984–10992. Kelly, R.A., Jakeman, A.J., Barreteau, O., Borsuk, M.E., ElSawah, S., Hamilton, S.H., Henriksen, H.J., Kuikka, S., Maier, H.R., Rizzoli, A.E., van Delden, H., Voinov, A.A., 2013. Selecting among five common modelling approaches for integrated environmental assessment and management. Environ. Model. Softw. 47 (0), 159–181. Kerr, G.L., Bjornlund, H., 2011. Understanding the acceptance of market-based instruments for the ecosystem service of water quality. WIT Trans. Ecol. Environ. 153, 173–184. Kotir, J.H., Smith, C., Brown, G., Marshall, N., Johnstone, R., 2016. A system dynamics simulation model for sustainable water resources management and agricultural development in the Volta River Basin, Ghana. Sci. Total Environ. 573, 444–457. Lynch, A.H., Adler, C.E., Howard, N.C., 2014. Policy diffusion in arid Basin water management: a Q method approach in the Murray–Darling Basin, Australia. Reg. Environ. Change 14, 1601–1613. Malard, J.J., Inam, A., Hassanzadeh, E., Adamowski, J., Tuy, H.A., Melgar-Quiñonez, H., 2017. Development of a software tool for rapid, reproducible, and stakeholderfriendly dynamic coupling of system dynamics and physically-based models. Environ. Model. Softw. 96, 410–420. McLellan, E.L., Schilling, K.E., Wolter, C.F., Tomer, M.D., Porter, S.A., Magner, J.A., Smith, D.R., Prokopy, L.S., 2018. Right practice, right place: a conservation planning toolbox for meeting water quality goals in the Corn Belt. J. Soil Water Conserv. 73, 29–34. McRae, M., 2013. Drainage Stewardship, Upper Souris Watershed Associations. http:// lowersouris.com/wp-content/uploads/2016/06/Final_Drainage_Stewardship_Book. pdf. Ministry of Agriculture, 2018. https://www.saskatchewan.ca/government/governmentstructure/ministries/agriculture. Mirchi, A., Madani, K., Watkins, D., Ahmad, S., 2012. Synthesis of system dynamics tools for holistic conceptualization of water resources problems. Water Resour. Manag. 26, 2421–2442. Moose Jaw River Stewards, 2018. http://www.mjriver.com/. Morales‐Marín, L.A., Wheater, H.S., Lindenschmidt, K.E., 2015. Assessing the transport of total phosphorus from a prairie river basin using SPARROW. Hydrol. Process. 29, 4144–4160. Morales-Marín, L.A., Wheater, H.S., Lindenschmidt, K.E., 2017. Assessment of nutrient loadings of a large multipurpose prairie reservoir. J. Hydrol. 550, 166–185. Ontario Ministry of Agriculture Food and Rural Affairs, 2007. Nutrient Management Protocol. http://www.omafra.gov.on.ca/english/nm/regs/nmpro/nmprotc-07.htm. Palm-Forster, L.H., Swinton, S.M., Shupp, R.S., 2017. Farmer preferences for conservation

1126